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The World's Smartest People Are Sprinting for the Same Exit

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The World's Smartest People Are Sprinting for the Same Exit

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430 segments

0:00

Right now, the most valuable companies

0:01

on Earth are racing to sell you their

0:03

stock. It's more money, bigger names,

0:05

and higher valuations than anything the

0:07

stock market has ever seen. To some

0:09

people, this is the opportunity of a

0:11

lifetime. The greatest technology boom

0:13

in history is finally opening its doors.

0:15

For decades, the best companies stayed

0:17

private, growing in the dark, and were

0:19

out of reach for ordinary people. But

0:21

now at last, the doors are being thrown

0:23

open, and you get the chance to own a

0:25

piece of the businesses that could

0:26

define the next 100 years. But to

0:28

others, this isn't a celebration at all.

0:31

It's a trap. That's the side I'm on, and

0:33

by the end of this video, I'm fairly

0:35

confident you'll be on this side, too.

0:37

Because in the stock market, an open

0:38

door is never a favor. Generosity is

0:40

never just out of the kindness of

0:42

someone's heart. It's like nobody

0:44

stopped to consider that in finance,

0:46

every trade needs a counterparty. Every

0:48

buyer needs a seller. So, if you're

0:50

trying to buy stock, someone has to be

0:52

willing to sell it to you. Which means

0:54

before you buy what they're selling,

0:55

there's one simple question worth

0:57

asking. Why are they selling? In an

0:59

initial public offering, the people on

1:01

the other side of the trade aren't

1:03

random. They're the founders who built

1:05

these companies, the early employees who

1:07

gave them years of their lives, the

1:08

venture capitalists who funded them when

1:10

they were nothing. Nobody on Earth

1:12

understands these businesses better than

1:14

they do, and they've seen the numbers

1:15

you and I will never get to see. And

1:17

they've looked at the most exciting

1:19

companies of our lifetime, the ones

1:20

everyone are desperate to own, and they

1:22

made a decision. They'd rather have your

1:24

cash than their own shares. This is what

1:27

an IPO actually is. Strip away the

1:29

headlines and the celebration, and it's

1:31

a trade with massive information

1:33

asymmetry. The sellers are the most

1:35

informed people in the room, which means

1:37

the buyer, by definition, is the least.

1:40

And right now, that buyer is you. So,

1:43

here's the thought that should be

1:44

lighting up in the back of your mind,

1:45

the one that Elon Musk, Sam Altman, and

1:48

everyone else behind this wave are

1:50

quietly counting on you not to ask. If

1:53

these really are the most valuable, most

1:55

important companies in the world, and

1:57

the future is as bright as everyone

1:59

keeps promising, then why are the people

2:01

who know them best in such a hurry to

2:03

sell them to you? And there's an answer.

2:05

It's a pattern that's repeated

2:06

throughout the history of the stock

2:08

market. It's shown up again and again

2:10

right before nearly every major

2:12

financial bubble pops, and most people

2:14

have no idea it's sitting right in front

2:16

of their eyes. I call it the dine and

2:18

dash. In poker, a tell is a small

2:21

unconscious signal that gives away what

2:23

someone actually believes, no matter

2:25

what they're saying out loud. Well,

2:27

markets have tells, too. They don't come

2:29

from headlines or earnings calls, and

2:31

they definitely don't come from CNBC.

2:34

They come from watching what people

2:35

actually do with their money. That's one

2:37

of the clearest tells there is, because

2:40

the average investor sells when they get

2:42

scared, when the news turns or the chart

2:44

drops, when everyone around them is

2:46

already running for the door. But, the

2:47

professionals do the exact opposite. As

2:50

Benjamin Graham, the father of value

2:51

investing and Warren Buffett's mentor,

2:53

said, "The intelligent investor is a

2:55

realist who sells to optimist and buys

2:57

from pessimist." They sell when

2:59

everything looks perfect, when the story

3:01

is loudest, the optimism is highest, and

3:03

there's a line of buyers out the door

3:05

begging to get in, because that's the

3:07

moment you can get the most money for

3:09

the thing you're selling, which is

3:10

exactly what the IPO data shows us. When

3:13

markets are booming, companies rush to

3:15

go public and sell shares. But, when

3:17

markets turn south, those IPOs disappear

3:20

faster than my situationship the second

3:22

I ask her, "So, what are we?" That's the

3:24

top being sold to you. And like I

3:26

mentioned earlier, we've seen this

3:28

pattern again and again throughout

3:30

history. Let's go back to June of 2007.

3:33

Wall Street was booming, the markets

3:35

were hot, and the two founders of

3:36

Blackstone, Stephen Schwarzman and Peter

3:39

Peterson, decided it was time to cash in

3:41

on some of it. Side note, who looks at a

3:43

newborn and lands on the name Peter

3:45

Peterson? Anyways, they took Blackstone

3:47

public, and between them, the two

3:49

co-founders walked away with about 2.6

3:51

billion dollars in cash. Then, a little

3:54

over a year later, the global financial

3:56

crisis hit, the markets crashed, and

3:58

Blackstone's stock plummeted roughly

4:00

90%. It's almost like the soon-to-be

4:02

world's largest alternative asset

4:04

manager knew it was time to exit out of

4:06

some assets. Or we can go back a little

4:08

further to the dot-com bubble. In the

4:10

years leading up to the dot-com crash,

4:12

the number of companies going public

4:14

exploded. And in 1999, the year before

4:17

the bubble popped, the concentration hit

4:19

a record. Of every company that went

4:21

public that year, around 51% came from a

4:23

single sector, tech. Then the year after

4:26

all of them listed, after the insiders

4:28

got rich and retail thought they were

4:29

buying a piece of the digital future,

4:32

the bubble popped. Over the next 2 and

4:34

1/2 years, the Nasdaq crashed more than

4:36

75% and an estimated $5 trillion in

4:39

market value simply evaporated. It took

4:41

15 years for the Nasdaq to recover. And

4:44

most of the companies that sold stock on

4:45

the way up never made it to the other

4:47

side. A study by the Kauffman Foundation

4:50

and Professor Jay Ritter, the man known

4:52

as Mr. IPO, found that 10 years after

4:54

going public, only 29% of the emerging

4:57

growth companies that went public

4:58

between 1996 and 2000 were still

5:01

standing as independent public

5:03

companies. The rest were gone. The thing

5:05

people thought they were buying no

5:07

longer existed. But in 2021, we saw the

5:10

same tail show up wearing a different

5:11

costume. But before I tell you what that

5:13

costume was, a quick pause. Because that

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costume. Because in 2021, this same tale

6:42

disguised itself as something Wall

6:44

Street swore was the future. The SPAC.

6:46

These are companies that go public

6:48

before they're actually a company. You

6:50

raise money from investors first, list

6:52

on the exchange, and then go shopping

6:54

for a real business to merge with. It's

6:56

an empty shell with a checkbook, which

6:58

is why people call them blank check

7:00

companies. The appeal was simple. A SPAC

7:02

let a private company reach the public

7:04

market without sitting through the

7:05

scrutiny of a normal IPO. It skipped a

7:08

lot of the normal vetting process. So,

7:10

the money came rushing in. In 2021

7:13

alone, 613 SPACs went public and raised

7:16

more than 160 billion dollars. For a

7:19

year, these blank check shells made up

7:21

more than half of every new company

7:23

hitting the US market. And the story

7:25

ends exactly how you'd expect. Companies

7:27

that completed a SPAC merger in 2021

7:30

lost 67% of their value on average. For

7:33

those that de-SPACed in 2022, the

7:36

average loss was 59%. And just like we

7:39

saw in on com the activity collapsed

7:42

right after the rug got pulled out from

7:44

underneath everyone. From 248 SPACs in

7:47

2020 to 613 in 2021 to just 86 in 2022.

7:53

That's the dine and dash. When markets

7:55

are booming, insiders are selling. When

7:57

markets aren't, they aren't selling.

7:58

It's a cycle we've seen over and over.

8:01

So, what about AI? There's still a

8:03

question that needs answering. Why now?

8:05

Why are the most valuable companies in

8:07

the world, fierce rivals who agree on

8:09

almost nothing, all racing to sell you

8:11

stock at the exact same moment? It comes

8:14

down to two numbers. The first being the

8:16

spending. I covered this in a previous

8:17

video, but since 88% of the people

8:20

watching this aren't subscribed, here's

8:22

the quick version. This year, the

8:24

largest technology companies on Earth

8:26

are on track to spend around $725

8:28

billion building out artificial

8:30

intelligence. That's up roughly 77% from

8:33

the year before. It's the largest and

8:35

fastest infrastructure build out in

8:37

history, but most people struggle to

8:39

even conceptualize how much $725 billion

8:43

actually is. So, I'll use the same

8:45

example I used in my previous video

8:48

because I think it captures the

8:49

absurdity of this better than anything

8:51

else. If you spent $1 million

8:53

every single day starting from the day

8:55

Jesus was born all the way through the

8:57

fall of the Roman Empire, the Middle

8:59

Ages, the Renaissance, both World Wars,

9:02

and every single day up to right now,

9:04

you still wouldn't have spent what Big

9:06

Tech alone will spend on AI in just

9:08

2026. It breaks down to roughly $2

9:11

billion a day, 83 million an hour, or

9:14

about $23,000 every second. But, there's

9:17

also a second number that perfectly sums

9:19

up the AI industry, and it's a number

9:21

you probably have never seen. It's what

9:23

all that spending is actually earning

9:25

back. The research firm Exponential View

9:28

put together the first real bottom-up

9:30

estimate of the entire AI economy. Over

9:32

the past 12 months, all of it combined

9:34

generated an estimated $110 billion in

9:37

revenue, and that's revenue, not profit.

9:40

Put another way, the entire AI economy

9:43

earned back an amount equal to roughly

9:45

15% of what Big Tech alone plans to

9:47

spend this year. By any standard, that's

9:49

a horrible investment. But I know what

9:51

some of you are probably thinking.

9:53

That's mostly Big Tech. Surely it's

9:55

better for everyone else, like the

9:56

actual businesses using this stuff. And

9:59

the answer is no. It's actually far

10:00

worse. Deloitte surveyed more than 3,000

10:03

business and IT leaders across 24

10:05

countries. They found that 74% of

10:08

organizations want AI to grow their

10:09

revenue, but only 20% have actually seen

10:12

it happen. Another study by researchers

10:14

at MIT looked even closer. They studied

10:16

hundreds of real corporate AI rollouts,

10:19

actual companies spending real money to

10:20

put this technology to work, and they

10:22

found that 95% of them produced zero

10:25

measurable return. So, put those numbers

10:27

side by side. $725 billion going in,

10:31

almost nothing coming back out. That gap

10:34

between what's being spent and what's

10:36

being earned is what nobody seems to be

10:38

watching. In fact, the market has priced

10:40

these companies as if that gap doesn't

10:42

even exist. Take OpenAI, the company

10:44

sitting in the eye of the storm. OpenAI

10:46

is still private, but the financials

10:48

leaked, and in June they filed

10:50

confidentially to go public, so the

10:51

picture's clear. Last year, OpenAI

10:54

brought in around $13 billion in

10:56

revenue, and before you say, "Pretty

10:58

good." Well, the company nearly tripled

11:00

that in losses. In 2025, OpenAI had a

11:04

net loss attributable to the company of

11:06

38 and a half billion dollars. By

11:08

OpenAI's own internal estimates, they

11:10

expect to burn well over $100 billion

11:13

between now and 2029. And here's the

11:15

cherry on top. On top of all of these

11:18

horrible fundamentals, the valuation is

11:20

worse. OpenAI's last funding round

11:23

valued the company at $852 billion,

11:26

more than 65 times its revenue. Another

11:29

side note, how did ChatGPT know I was

11:32

talking about itself here? Anyways,

11:34

these aren't bets on whether these are

11:35

good companies. These are bets that ride

11:37

on blind optimism, which reminds me of

11:40

what goes down as maybe the funniest

11:41

quote in finance history. And if you

11:43

haven't heard of this story, you're in

11:44

for a treat. It even beats out the Jamie

11:46

Dimon cockroach story from a few videos

11:48

ago. One of the people who stood at the

11:50

very center of what's considered the

11:52

worst financial crisis in history

11:54

admitted a crisis was coming out loud on

11:56

the record right before everything

11:58

broke. In the summer of 2007, months

12:01

before the entire global financial

12:03

system nearly collapsed, Charles Prince,

12:06

the CEO of Citigroup at the time, was

12:08

asked about the bank's continued

12:09

exposure to risky lending and deal

12:11

financing, even as the credit markets

12:13

were starting to look increasingly

12:15

stretched. His answer became one of the

12:17

most infamous lines in the history of

12:19

finance. He said, "When the music stops,

12:21

in terms of liquidity, things will be

12:23

complicated, but as long as the music is

12:25

playing, you've got to get up and dance.

12:27

We're still dancing." So, he wasn't

12:29

saying the party would last. He actually

12:32

was saying the opposite. He knew it

12:33

wouldn't. He was saying as long as the

12:35

money was flowing, he was going to keep

12:37

dancing anyway, because the second the

12:39

music stopped, whoever was still on the

12:40

floor would be the one who lost. He

12:43

understood that underneath all the

12:44

dancing, it was really a giant game of

12:46

hot potato, and the only thing that

12:48

mattered was making sure you'd pass the

12:50

hot potato on to someone else before the

12:52

music cut out, which is exactly what

12:54

you're watching now. Except the potato

12:56

is stock in an AI company priced for

12:58

perfection. The players are the founders

13:00

and the insiders, passing it to the next

13:02

eager buyer. Every one of them knows

13:04

someone eventually gets left holding it,

13:06

but none of them know exactly when the

13:08

passing stops. So, the only rational

13:10

move is to hand it off now to the

13:12

biggest, most enthusiastic crowd of

13:14

buyers they can possibly find, the

13:17

United States stock market. So, let's go

13:19

back to the beginning. At the start of

13:21

this video, I asked you a question. If

13:23

these really are the most valuable

13:25

companies in the world, and the future

13:27

is as bright as everyone keeps

13:29

promising, then why are the people who

13:31

know them best in such a hurry to sell?

13:34

We've spent this whole video circling

13:35

that question and giving context. Now we

13:38

can finally answer it. It's because it's

13:39

the smart thing to do. The people who

13:41

run these companies aren't dumb. They

13:43

can see the gap. They see that prices

13:45

have sprinted miles ahead of the

13:47

fundamentals. They know that the market

13:49

has priced these companies for a future

13:51

where every one of these bets pay off

13:53

perfectly. But they're also in the

13:55

middle of an AI arms race, so they need

13:57

deep pockets and an appetite for a bet

13:59

with a slim chance of ever paying off

14:01

can only last for so long, which means

14:03

whoever sells first gets to sell into

14:05

the hungry crowd. It doesn't actually

14:07

matter if these companies are good or

14:08

not. The price already assumed it.

14:10

There's little room left for good news

14:12

and enormous room left for

14:13

disappointment. Eventually the

14:15

fundamentals will catch up. They always

14:17

do, but I can't tell you when. I don't

14:19

have a magic stopwatch that can predict

14:21

when this bubble pops. I don't know if

14:23

it's in a month, in a year, or 3 years

14:25

from now. I don't know if these

14:26

companies grind higher for a long time

14:29

before any of it matters. Nobody knows

14:31

that and anyone who stares into a

14:33

camera, hands you a date, and acts like

14:35

they do know is lying to you. The

14:37

reality is you don't need the date. You

14:40

just need to be able to read the room,

14:41

which is the reason I made this video

14:43

and my channel in general is to help you

14:45

read the room you're standing in so

14:47

you're not the last one to notice the

14:48

music. And if that's worth something to

14:50

you and the version of finance you

14:52

actually want, hit subscribe. Because

14:54

you don't need to know the exact moment

14:56

the music stops to know one thing. You

14:58

don't want to be the last one on the

14:59

dance floor when it does.

15:03

>> [music]

Interactive Summary

This video explores the dynamics of stock market IPOs and why insiders are often eager to sell their shares to the public during periods of high optimism. Using historical examples like the dot-com bubble, the 2007 financial crisis, and the 2021 SPAC craze, the video argues that an 'information asymmetry' often exists where insiders cash out at high valuations before market corrections. The video specifically examines the current AI investment boom, highlighting the massive disparity between the huge infrastructure spending and the relatively low revenue generated, ultimately suggesting that investors should be cautious of market signals and the 'dine and dash' cycle.

Suggested questions

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